7th International Conference on Cognitive Radio Oriented Wireless Networks

Research Article

TV White Space Channel Allocation with Simulated Annealing as Meta Algorithm

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  • @INPROCEEDINGS{10.4108/icst.crowncom.2012.248570,
        author={Bo Ye and Maziar Nekovee and Anjum Pervez and Mohammad Ghavami},
        title={TV White Space Channel Allocation with Simulated Annealing as Meta Algorithm},
        proceedings={7th International Conference on Cognitive Radio Oriented Wireless Networks},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2012},
        month={7},
        keywords={simulated annealing hyper-heuristics algorithm channel allocation tv white space},
        doi={10.4108/icst.crowncom.2012.248570}
    }
    
  • Bo Ye
    Maziar Nekovee
    Anjum Pervez
    Mohammad Ghavami
    Year: 2012
    TV White Space Channel Allocation with Simulated Annealing as Meta Algorithm
    CROWNCOM
    IEEE
    DOI: 10.4108/icst.crowncom.2012.248570
Bo Ye1,*, Maziar Nekovee2, Anjum Pervez1, Mohammad Ghavami1
  • 1: London South Bank University
  • 2: BT Research
*Contact email: yebo0125@gmail.com

Abstract

When developing a TV White Space (TVWS) system with the available TV spectrum after digital switchover, channel allocation for TVWS devices to avoid interference becomes one of the most challenging problems. In this paper, Simulated Annealing (SA) is applied to solve the problem to minimize the total interference. However, the performance of SA applied to the problem depends on the appropriate choice of several key parameters. Hence, all parameters are listed and experiments with all possible combinations are done and the parameters that perform best are chosen manually. Manual selection requires a lot of experiments and is not effective. Therefore, an algorithm using SA as meta algorithm is proposed to choose the parameters automatically for SA. Finally the result of parameters selected automatically is compared with the result of those selected manually. We will show that automatic selection performs better compared with manual selection.